Detection and Parameter Estimation of Multiple NonGaussian Sources via Higher Order Statistics

Sanyogita Shamsunder; Georgios B. Giannakis

doi:10.1109/78.295204

Detection and Parameter Estimation of Multiple NonGaussian Sources via Higher Order Statistics

Sanyogita Shamsunder, Georgios B. Giannakis

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13 Scopus citations

Abstract

Simultaneous detection of signals arriving at a sensor array and estimation of their parameters is carried out using higher than second-order statistics. Information theoretic criteria which are (at least theoretically) insensitive to additive Gaussian noise are developed to estimate consistently the parameters as well as the number of non-Gaussian but unknown sources. The novel cumulant based algorithms can estimate parameters of more sources with fewer sensors. Simulations confirm superior resolution capability of the proposed methods for both narrow-band and wideband sources in the presence of low SNR additive correlated Gaussian noise.

Original language	English (US)
Pages (from-to)	1145-1155
Number of pages	11
Journal	IEEE Transactions on Signal Processing
Volume	42
Issue number	5
DOIs	https://doi.org/10.1109/78.295204
State	Published - May 1994
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received July 8, 1992; revised June 28, 1993. The associate editor coordinating the review of this paper and approving it for .publication was Prof. Daniel Fuhrmann. This work was supported by LABCOM contract 5-25254 and NSFMIP-9210230.

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Detection and Parameter Estimation of Multiple NonGaussian Sources via Higher Order Statistics

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